Python Runtime Method Call Binding

[Bruno Desthuilliers]

k3xji a écrit :
(snip)
> My problem is that I have a very-multithreaded application that I want
> to profile dynamically. And I want to see how many functions are
> called/how much time spent on them.(I tried Profilers but lack of
> multithreading support I cannot do what I want to do.)

???

I didn't found any mention of such a limitation on the profile / 
cProfile doc:
http://www.python.org/doc/2.6/library/profile.html#module-cProfile

> So, from your answers above, if I use a decorator  I will not be able
> to understand that the function has finished processing.

Yes you will:

def log(func):
     def _logged(*args, **kw):
         print "func", func.__name__, " called with ", args, kw
         result = func(*args, **kw)
         print "func", func.__name__, " returned ", result
         return result

     _logged.__name__ = "logged_%s" % func.__name__
     _logged.__doc__ = func.__doc__
     return _logged

 > I think problem is clarified now. Maybe you have other suggestions?


Yes.

1/ the

@decorator
def func():
    pass

syntax is really syntactic sugar for

def func():
    pass

func = decorator(func)


2/ nothing prevents you from dynamically rebinding methods at runtime:

def log(func):
     # cf below
     if getattr(func, "_decorated", False):
         # already decorated, just return the func or method as is
         return func

     def _logged(*args, **kw):
         print "func", func.__name__, " called with ", args, kw
         result = func(*args, **kw)
         print "func", func.__name__, " returned ", result
         return result

     _logged.__name__ = "logged_%s" % func.__name__
     _logged.__doc__ = func.__doc__

     # IMPORTANT : mark the function / method as already decorated
     _logged._decorated = True

     return _logged


class Foo(object):
     def bar(self):
        pass

Foo.bar = log(Foo.bar)

3/ Now fact is that even all this won't probably be enough to implement 
a robust generic profiler - something which obviously requires a deep 
understanding of the language *and* it's implementation.